1. Home
  2. Vol 7, No 4 (2023)
  3. Muthaiyah

A Binary Survivability Prediction Classification Model towards Understanding of Osteosarcoma Prognosis

Saravanan Muthaiyah, Vivek Ajit Singh, Thein Oak Kyaw Zaw, Kalaiarasi S. M. Anbananthen, Byeonghwa Park, Myung Joon Kim

Abstract


The objective of this study is to explore effective and innovative machine learning techniques that can assist medical professionals in developing more accurate prognoses that can enhance the survivability of osteosarcoma patients by investigating potential prognostic factors and identifying novel therapeutic approaches. A comprehensive analysis was conducted using a dataset of 128 osteosarcoma patients between 1997 to 2011. The dataset included 52 attributes in total that covered a wide range of demographics, together with information on clinical records, treatment protocols, and survival outcomes. Data was obtained from NOCERAL (National Orthopaedic Centre of Excellence in Research and Learning), Kuala Lumpur. Three distinct binary classification methods (i.e., random forest, support vector machine (SVM), and artificial neural network (ANN)) were employed to identify the prognostic factors that are associated with improved survival efficacy measures. The results of this study revealed that both SVM and ANN outperformed random forests in predicting survivability for both the 2-year and 5-year time frames. These findings indicate the potential of SVM and ANN as effective tools for predicting osteosarcoma survivability. The study signifies a significant step towards integrating machine learning techniques into the existing toolkit available to medical practitioners. This study contributes to the medical field by providing a comparative analysis of three prominent machine learning techniques for predicting osteosarcoma survivability. The superior performance of SVM and ANN over random forests highlights the potential of these methods in generating more accurate survivability predictions. Further development and refinement of these machine learning techniques hold promise for enhancing their effectiveness and instilling greater confidence among medical professionals and patients in the predictive capabilities of machine learning and artificial intelligence models for osteosarcoma survivability.

 

Doi: 10.28991/ESJ-2023-07-04-018

Full Text: PDF


Keywords


Osteosarcoma; Survivability; Prognosis; Machine Learning.

References


Merriam-Webster. (2023). Prognosis. “Merriam-Webster.Com” Dictionary. Available online: https://www.merriam-webster.com/dictionary/prognosis (accessed on April 2023).

United Nations. (2014). Health-United Nations Sustainable Development. United Nations, New York, United States. Available online: https://www.un.org/sustainabledevelopment/health/ (accessed on April 2023).

Mackillop, W. J. (2006). The Importance of Prognosis in Cancer Medicine. TNM Online. doi10.1002/0471463736.tnmp01.pub2.

Mirabello, L., Troisi, R. J., & Savage, S. A. (2009). Osteosarcoma incidence and survival rates from 1973 to 2004: Data from the surveillance, epidemiology, and end results program. Cancer, 115(7), 1531–1543. doi:10.1002/cncr.24121.

Chao, C. M., Yu, Y. W., Cheng, B. W., & Kuo, Y. L. (2014). Construction the Model on the Breast Cancer Survival Analysis Use Support Vector Machine, Logistic Regression and Decision Tree. Journal of Medical Systems, 38(10), 106. doi:10.1007/s10916-014-0106-1.

Roshani, S., Coccia, M., & Mosleh, M. (2022). Sensor Technology for Opening New Pathways in Diagnosis and Therapeutics of Breast, Lung, Colorectal and Prostate Cancer. HighTech and Innovation Journal, 3(3), 356-375. doi:10.28991/HIJ-2022-03-03-010.

Montazeri, M., Montazeri, M., Montazeri, M., & Beigzadeh, A. (2016). Machine learning models in breast cancer survival prediction. Technology and Health Care, 24(1), 31–42. doi:10.3233/THC-151071.

Kansara, M., Teng, M. W., Smyth, M. J., & Thomas, D. M. (2014). Translational biology of osteosarcoma. Nature Reviews Cancer, 14(11), 722–735. doi:10.1038/nrc3838.

Misaghi, A., Goldin, A., Awad, M., & Kulidjian, A. A. (2018). Osteosarcoma: A comprehensive review. Sicot-J, 4, 12. doi:10.1051/sicotj/2017028.

Lindsey, B. A., Markel, J. E., & Kleinerman, E. S. (2017). Osteosarcoma Overview. Rheumatology and Therapy, 4(1), 25–43. doi:10.1007/s40744-016-0050-2.

Stewart, B. W., & Wild, C. P. (2014). World Cancer Report 2014. World Health Organization (WHO), Geneva, Switzerland.

Tharakan, S., Raja, I., Pietraru, A., Sarecha, E., Gresita, A., Petcu, E., Ilyas, A., & Hadjiargyrou, M. (2023). The Use of Hydrogels for the Treatment of Bone Osteosarcoma via Localized Drug-Delivery and Tissue Regeneration: A Narrative Review. Gels, 9(4), 274. doi:10.3390/gels9040274.

Tang, J., Wang, J. K., & Pan, X. (2022). A Web-Based Prediction Model for Overall Survival of Elderly Patients with Malignant Bone Tumors: A Population-Based Study. Frontiers in Public Health, 9, 1-12. doi:10.3389/fpubh.2021.812395.

Siegel, R. L., Miller, K. D., & Jemal, A. (2020). Cancer statistics, 2020. CA: A Cancer Journal for Clinicians, 70(1), 7–30. doi:10.3322/caac.21590.

Jiang, J., Pan, H., Li, M., Qian, B., Lin, X., & Fan, S. (2021). Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm. Scientific Reports, 11, 5542. doi:10.1038/s41598-021-85223-4.

Muthaiyah, S., Singh, V.A. (2021). Bone Cancer Survivability Prognosis with KNN and Genetic Algorithms. Concepts and Real-Time Applications of Deep Learning. EAI/Springer Innovations in Communication and Computing. Springer, Cham, Switzerland. doi.org/10.1007/978-3-030-76167-7_8.

Park, K., Ali, A., Kim, D., An, Y., Kim, M., & Shin, H. (2013). Robust predictive model for evaluating breast cancer survivability. Engineering Applications of Artificial Intelligence, 26(9), 2194–2205. doi:10.1016/j.engappai.2013.06.013.

Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., Schroter, S., Riley, R. D., Hemingway, H., & Altman, D. G. (2013). Prognosis Research Strategy (Progress) 3: Prognostic Model Research. PLoS Medicine, 10(2), e1001381. doi:10.1371/journal.pmed.1001381.

Kyburz, D., & Finckh, A. (2013). The importance of early treatment for the prognosis of rheumatoid arthritis. Swiss Medical Weekly, 143. doi:10.4414/smw.2013.13865.

Harting, M. T., Lally, K. P., Andrassy, R. J., Vaporciyan, A. A., Cox, C. S., Hayes-Jordan, A., & Blakely, M. L. (2010). Age as a prognostic factor for patients with osteosarcoma: An analysis of 438 patients. Journal of Cancer Research and Clinical Oncology, 136(4), 561–570. doi:10.1007/s00432-009-0690-5.

LeCornu, M. G., Chuang, S. K., Kaban, L. B., & August, M. (2011). Osteosarcoma of the jaws: Factors influencing prognosis. Journal of Oral and Maxillofacial Surgery, 69(9), 2368–2375. doi:10.1016/j.joms.2010.10.023.

Duchman, K. R., Gao, Y., & Miller, B. J. (2015). Prognostic factors for survival in patients with high-grade osteosarcoma using the Surveillance, Epidemiology, and End Results (SEER) Program database. Cancer Epidemiology, 39(4), 593–599. doi:10.1016/j.canep.2015.05.001.

Kim, J., & Shin, H. (2013). Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data. Journal of the American Medical Informatics Association, 20(4), 613–618. doi:10.1136/amiajnl-2012-001570.

Wang, P., Li, Y., & Reddy, C. K. (2019). Machine Learning for Survival Analysis. ACM Computing Surveys, 51(6), 1–36. doi:10.1145/3214306.

Dudley, W. N., Wickham, R., & Coombs, N. (2016). An Introduction to Survival Statistics: Kaplan-Meier Analysis. Journal of the Advanced Practitioner in Oncology, 7(1), 91-100. doi:10.6004/jadpro.2016.7.1.8.

Stel, V. S., Dekker, F. W., Tripepi, G., Zoccali, C., & Jager, K. J. (2011). Survival analysis I: The Kaplan-Meier method. Nephron - Clinical Practice, 119 (1): c83–c88. doi:10.1159/000324758.

Etikan, I. (2017). The Kaplan Meier Estimate in Survival Analysis. Biometrics & Biostatistics International Journal, 5(2), 55-59. doi:10.15406/bbij.2017.05.00128.

Datema, F. R., Moya, A., Krause, P., Bäck, T., Willmes, L., Langeveld, T., Baatenburg De Jong, R. J., & Blom, H. M. (2012). Novel head and neck cancer survival analysis approach: Random survival forests versus cox proportional hazards regression. Head and Neck, 34(1), 50–58. doi:10.1002/hed.21698.

Li, L., Yang, Z., Hou, Y., & Chen, Z. (2020). Moving beyond the Cox proportional hazards model in survival data analysis: A cervical cancer study. BMJ Open, 10(7), e033965. doi:10.1136/bmjopen-2019-033965.

Matsuo, K., Purushotham, S., Jiang, B., Mandelbaum, R. S., Takiuchi, T., Liu, Y., & Roman, L. D. (2019). Survival outcome prediction in cervical cancer: Cox models vs. deep-learning model. American Journal of Obstetrics and Gynecology, 220(4), 381.e1-381.e14. doi:10.1016/j.ajog.2018.12.030.

Miladinovic, B., Kumar, A., Mhaskar, R., Kim, S., Schonwetter, R., & Djulbegovic, B. (2012). A Flexible Alternative to the Cox Proportional Hazards Model for Assessing the Prognostic Accuracy of Hospice Patient Survival. PLoS ONE, 7(10), e47804. doi:10.1371/journal.pone.0047804.

Shafique, U., & Qaiser, H. (2014). A comparative study of data mining process models (KDD, CRISP-DM and SEMMA). International Journal of Innovation and Scientific Research, 12(1), 217-222.

Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016). A survey of machine learning for big data processing. Eurasip Journal on Advances in Signal Processing, 2016(1), 2351–8014. doi:10.1186/s13634-016-0355-x.

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17. doi:10.1016/j.csbj.2014.11.005.

Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017). Disease Prediction by Machine Learning over Big Data from Healthcare Communities. IEEE Access, 5, 8869–8879. doi:10.1109/ACCESS.2017.2694446.

Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., & Hua, L. (2012). Data mining in healthcare and biomedicine: A survey of the literature. Journal of Medical Systems, 36(4), 2431–2448. doi:10.1007/s10916-011-9710-5.

Li, Z., Soroushmehr, S. M. R., Hua, Y., Mao, M., Qiu, Y., & Najarian, K. (2017). Classifying osteosarcoma patients using machine learning approaches. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea. doi:10.1109/embc.2017.8036768.


Full Text: PDF

DOI: 10.28991/ESJ-2023-07-04-018

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Saravanan Muthaiyah, Vivek Singh, Thein Oak Kyaw Zaw, Kalaiarasi Sonai Muthu Anbananthen, Byeonghwa Park, Myung Joon Kim